code: def convert_2d(self, pred2, tforms, meta):
      is_flip = meta["flip"]
      tforms = tforms.cpu().numpy()
      pred2 = pred2.cpu().numpy()
      B = pred2.shape[0]
      points2d = (pred2.reshape(B, -1, 2) + 1.0) * self.input_size // 2
      if is_flip:
          points2d = points2d[:, self.flipindex, :]
          points2d[:, :, 0] = self.input_size - 1 - points2d[:, :, 0]
      # B = points2d.shape[0]
      points2de = np.ones(
          (points2d.shape[0], points2d.shape[1], 3),
          dtype=points2d.dtype,
      )
      points2de[:, :, :2] = points2d
      verts2d = np.zeros((B, points2d.shape[1], 2), dtype=np.float32)
      for n in range(B):
          tform = tforms[n]
          tform_inv = cv2.invertAffineTransform(tform)
          _points2d = np.dot(tform_inv, points2de[n].T).T
          verts2d[n] = _points2d
      # return verts2d, points2d
      return verts2d
  
imports: import os
  import time
  import timm
  import glob
  import numpy
  import os.path
  import cv2
  import torch
  import torch.distributed
  from torch import nn
  from pathlib import Path
  from backbones import get_network
  from skimage import transform
  from scipy.spatial.transform import Rotation
label_desc: Use image node_id 6 for calling the JMLRInference obj's underlying member method code with example usage: obj.convert_2d(pred2, tforms, meta) and returns: verts2d